Genetic Determinants of High-density Lipoprotein Cholesterol Efflux Capacity: Insights from Paraoxonase 1 Polymorphisms
Bibliographic record
Abstract
In recent years, it has been recognized that the quality, not just the quantity, of high-density lipoprotein (HDL) is important.Numerous studies have shown that the cholesterol efflux capacity (CEC) of HDL is a negative risk factor for cardiovascular disease independent of HDL cholesterol (HDL-C) levels 1,2) .Under conditions such as insulin resistance, oxidative stress, and chronic inflammation, changes in the composition of HDL components and posttranslational modifications of constituent proteins can impair HDL's anti-atherosclerotic effects 3) .However, according to a large-scale family-based population study, CEC is estimated to be 13% heritable and independent of HDL-C 4) .A genomewide association study (GWAS) conducted on 5,293 French-Canadian individuals identified significant genetic signals associated with CEC in 5 loci related to lipid biology, including CETP, LIPC, LPL, APOA1/C3/A4/A5, and APOE/C1/C2/C4, as well as near PPP1CB/PLB1 and RBFOX3/ENPP7.After adjusting for HDL-C and triglyceride levels, the remaining significant association was observed with genetic variants at the APOE/C1/C2/C4 locus 5) .In a GWAS involving 607 patients with coronary artery disease, the CDKAL1 locus was independently associated with CEC, distinct from HDL-C 6) .In addition, a GWAS of 4,981 patients with chronic kidney disease revealed associations between CEC and KLKB1 and CLSTN2 genes 7) .In contrast, endothelial lipase (EL), which preferentially hydrolyzes phospholipids in HDL, is encoded by the LIPG gene, and loss-of-function mutations in the LIPG gene have been shown to improve CEC alongside elevated HDL-C levels 8) .However, recent reports have identified pathogenic LIPG variants that impair the HDL function 9, 10) .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".